Adaptive neural network based sliding mode altitude control for a quadrotor UAV

Razmi, Hadi

doi:10.1007/s11771-018-3943-0

Cited by 13 publications

(1 citation statement)

References 27 publications

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“…The advantage of artificial intelligence has been exploited in [12], where the authors proposed a proportional-integral-derivative (PID) motion controller capable of maintaining proportions in critical factors. An analogous method was proposed by Razmi [13]. The work uses an adaptive sliding mode controller based on a neural network for flight altitude control.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment

Horváth

Gazda

Šlapak

et al. 2019

Entropy

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Attempts to develop flexible on-demand drone-assisted mobile network deployment are increasingly driven by cost-effective and energy-efficient innovations. The current stage opens up a wide range of theoretical discussions on the management of swarm processes, networks and other integrated projects. However, dealing with these complex issues remains a challenging task, although heuristic approaches are usually utilized. This article introduces a model of autonomous and adaptive drones that provide the function of aerial mobile base stations. Its particular goal is to analyze post-disaster recovery if the network failure takes place. We assume that a well-structured swarm of drones can re-establish the connection by spanning the residual functional, fixed infrastructure, and providing coverage of the target area. Our technique uses stochastic Langevin dynamics with virtual and adaptive forces that bind drones during deployment. The system characteristics of the swarms are a priority of our focus. The assessment of parametric sensitivity with the insistence on the manifestation of adaptability points to the possibility of improving the characteristics of the swarms in different dynamic situations.

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Section: Introductionmentioning

confidence: 99%